Then we study the influence of the amount of data without sensitive attribute labels on the model accuracy and fairness.
We vary the percentage of unlabeled data used in Semi-FairVAE and compare the model performance in Fig. REF .
From the results, we find that when more unlabeled data is used, the model fairness can be greatly improved, while the accuracy will slightly decrease.
This is because when more unlabeled data is used, the generative model can better estimate the joint distribution of input features and their sensitive attributes, and thereby can better remove the bias information to improve fairness.
It is also intuitive to have some accuracy sacrifice because removing the bias information also remove some learning shortcuts [1]}.
However, when the amount of unlabeled data is much greater than the number of labeled data, the fairness improvement brought by incorporating more unlabeled data becomes smaller while the performance sacrifice becomes more significant.
This is because the loss on labeled data may not be sufficiently optimized.
Thus, if used in practical scenarios, we need to choose a proper amount of unlabeled data to participate in the semi-supervised model learning to adjust the tradeoff between accuracy and fairness.
